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001			303770
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007			cr nn 008mamaa
008			150903s2012 gw | o |||| 0|eng d
020			_a9783642272257 _99783642272257
024	7		_a10.1007/9783642272257 _2doi
035			_avtls000358440
039		9	_a201509030606 _bVLOAD _c201405070231 _dVLOAD _y201402191521 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aHamelryck, Thomas. _eeditor. _9342452
245	1	0	_aBayesian Methods in Structural Bioinformatics / _cedited by Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2012.
300			_axxii, 385 páginas 86 ilustraciones, 7 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aStatistics for Biology and Health, _x1431-8776
500			_aSpringer eBooks
505	0		_aPart I Foundations: An Overview of Bayesian Inference and Graphical Models -- Monte Carlo Methods for Inferences in High-dimensional Systems -- Part II Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials -- Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures -- A Statistical View on the Reference Ratio Method -- Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution -- Statistics of Bivariate von Mises Distributions -- Bayesian Hierarchical Alignment Methods -- Likelihood and Empirical Bayes Superpositions of Multiple Macromolecular Structures -- Part IV Graphical models for structure prediction: Probabilistic Models of Local Biomolecular Structure and their Application in Structural Simulation -- Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields -- Part V Inferring Structure from Experimental Data -- Inferential Structure Determination from NMR Data -- Bayesian Methods in SAXS and SANS Structure Determination.
520			_aThis book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aMardia, Kanti. _eeditor. _9342453
700	1		_aFerkinghoff-Borg, Jesper. _eeditor. _9342454
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783642272240
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-27225-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c303770 _d303770